A Neural Embedding-based Recommender System to Get the Most out of EV Recharge Times-Reference-Cited by-同舟云学术

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A Neural Embedding-based Recommender System to Get the Most out of EV Recharge Times

Published:2023-03-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)
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Author:

Lucio Starace Luigi Libero¹,Bianco Luca¹,Di Martino Sergio¹

Affiliation:

1. Università degli Studi di Napoli Federico II,Naples,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10114798/10114807/10114855.pdf?arnumber=10114855

Reference28 articles.

1. Bert: Pre-training of deep bidirectional transformers for language understanding;devlin;Proceedings of NAACL-HLT,2019

2. Recommendation of geographic distributed charging stations for electric vehicles: A game theoretical approach

3. Smart parking guidance, monitoring and reservations: a review;amir;IEEE Intelligent Transportation Systems Magazine,2017

4. A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

5. Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Vehicular Crowdsensing with High-Mileage Vehicles: Investigating Spatiotemporal Coverage Dynamics in Historical Cities with Complex Urban Road Networks;Journal of Advanced Transportation;2023-05-12

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